Scaling GenAI projects with confidence

The Retrieval-Augmented Generation (R.A.G.) design pattern provides fine-grained control over the search and retrieval of internal information, ensuring the LLM functions solely as an interface layer. This minimizes unnecessary exposure of sensitive data while maximizing precision in responses.

Select the most suitable LLM based on specific use cases, cost-per-transaction constraints, and the contextual depth required, ensuring an optimal balance between performance and efficiency.

Ensure quality and reliability by tracking metrics across all layers—source data, search/retrieval, response generation, and application integration. The framework seamlessly integrates into ML/LLM Ops pipelines for continuous monitoring and improvement.

Leverage GenAI without sharing data back with cloud service providers (CSPs). Additionally, enable on-premises solutions using open-source LLMs, giving you full control over data privacy and infrastructure.

Enhance GenAI's healthcare relevance by embedding domain-specific knowledge from healthcare ontologies, internal dictionaries, terminologies, and other knowledge bases, ensuring outputs aligned with industry standards and organizational expertise.